Improving customer service and productivity at checkout continues to be a major strategy for retailers looking to grow in a competitive marketplace. Often, retailers need to balance these benefits against the necessity to provide security at checkout. Theft, or “shrink” (i.e., intentionally and unintentional), presents a confound to high checkout productivity as closely monitoring can be laborious and off-putting to customers, the vast majority of which are not involved in any illicit behavior.
Traditionally, a retailer's Loss Prevention team uses tellers or security personnel present at checkout, manual video surveillance to monitor the checkout process, or electronic article surveillance (EAS) tags on items to prevent shrink. However, having a person present and the manual video monitoring approach are labor intensive and, perhaps, are not completely practical to deter shrink at stores with a large number of point of sale (POS) devices. Further, thieves have learned how to circumvent EAS tags. Other non-traditional methods to prevent shrink at checkouts include video analytics or computer vision detection. Companies that provide these shrink detection methods include, for example, StopLift and Everseen. These methods can be effective, but require the theft events to be in clear sight of existing camera views or may require an additional investment of cameras at each POS. Also, until video analytic technology improves, most shrink detection video implementations include a manual validation step to confirm the event before taking actions. At self-service checkout (SSCO), most vendors, such as NCR, provide weight based item security and some vendors have versions of visual analysis, e.g., NCR's SmartAssist, Produce Assurance, and PickList Assist. However, many large retailers have disabled weight based item security in favor of allowing customers to be more productive and improve their experiences by significantly reducing attendant interventions. Furthermore, the visual analysis methods typically depend on the customer placing the item on or pass it by the scanner, but many shrink events at SSCOs involve the customer skipping the scanner and bagging item directly or leaving items in the shopping cart. Similar theft can also occur at teller-assisted checkout stations, such as among friends or by disgruntled employees.
Current solutions for detecting shrink, or at least a certain likelihood therefore, while helpful, remain deficient.